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GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM

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arxiv 2407.10870 v1 pith:3A3GQJ7H submitted 2024-07-15 cs.CV cs.AIcs.HCcs.LG

classification cs.CVcs.AIcs.HCcs.LG
keywords modelsfine-tuningfoundationforearmgpt-4ohandlargetasks
verification ladder T0 review T1 audit T2 compute T3 formal
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Large vision-language models (LVLMs), such as the Generative Pre-trained Transformer 4-omni (GPT-4o), are emerging multi-modal foundation models which have great potential as powerful artificial-intelligence (AI) assistance tools for a myriad of applications, including healthcare, industrial, and academic sectors. Although such foundation models perform well in a wide range of general tasks, their capability without fine-tuning is often limited in specialized tasks. However, full fine-tuning of large foundation models is challenging due to enormous computation/memory/dataset requirements. We show that GPT-4o can decode hand gestures from forearm ultrasound data even with no fine-tuning, and improves with few-shot, in-context learning.

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  1. Human Re-ID Meets LVLMs: What can we expect?

    cs.CV 2025-01 conditional novelty 4.0 of 10

    On a curated 20-query subset of Market1501, PersonViT strongly outperforms ChatGPT-4o, Gemini-2.0-Flash, Claude 3.5 Sonnet, and Qwen-VL-Max in separating genuine from impostor person matches.

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